1. Forecasting General Election Results in Poland 2011 on the basis of Social Media content Paweł Kuczma, Włodzimierz Gogołek Institute of Journalism University of Warsaw Graphic source: http://jonnewman12.files.wordpress.com/2010/05/sm-crystal-ball.png GOR 13, 4-6 March 2013 Baden-Wuerttemberg Cooperative State University Mannheim

2. General Online Research Conference GOR 13, 4-6 March 2013 Baden-Wuerttemberg Cooperative State University Mannheim, Germany Wlodzimierz Gogolek, Institute of Journalism, University of Warsaw Pawel Kuczma, Institute of Journalism, University of Warsaw Forecasting General Election Results in Poland 2011 on the basis of Social Media content Contact: p.kuczma@id.uw.edu.pl

3. Data Refining* Process Results Data from Social Media & News Portals Qualitative Analysis Quantitative Analysis Włodzimierz Gogołek, Refining network information (Rafinacja informacji sieciowej), [in:], Aleksander Jastriebow, Maria Raczyńska, Informatyka w dobie XXI wieku, Nauka, Technika, Edukacja a nowoczesne technologie informatyczne, Radom 2011, Politechnika Radomska, s. 229 - 238.

4. The purpose of this study: The purpose of this study was to define factors allowing prediction of the outcome of the general election in Poland in October 2011 basing on big data resources from Social Media websites in pre-election period in, what we call, data refining process. The research question was: Is it possible to predict the action (cast a vote for a political party in general election) on the basis of quantitative (number of content related to the subject of the research) and qualitative (the contexts in which they appear and their emotional values) content analysis on Social Media? Study Details - Methodology

5. Type of content analysed:  Social Media websites (such as social networking sites, forums, blogs and microblogs)  News portals (websites with content written by professionals) for comparative analysis Time of the study: Analy s ed content was published in pre-election period in 2011 (between March 1 st and Octobrer 31 st ) The following indicators of the content were examined :  Quantitative assessment: - Amount of the content about candidates, - Trends / dynamics of changes in the amount of the content,;  Qualitative assessment: - Contexts analysis (finding topic content), - Sentiment analysis (distinction between positive and negative content).

6. Quantitative assessment

7. Share of voice by different types of Social Media websites Most important sources were Blogs and Forums – 97% of content

8. Election results Source: State Electoral Comission PartyVote Share PO39,18% PiS29,89% Ruch Palikota10,02% SLD8,24% PSL8,36% PJN2,19%

9. Parties’ visibility online, especially in Social Media

10. Two the most popular parties in Social Media gained biggest users attention in this channel BlogsForums

11. Two the most popular parties in Social Media gained biggest users’ attention in both channels along with SLD BlogsForums

12. Qualitative assessment

13. Social Media context od content

14. Media contexts vs. Essential contexts Media contexts – which appear strongly in media during the campaign period Essential contexts – connected directly with powers of the government

15. Sentiment analysis in Social Media (forums) Data gathered: 1.03-31.10.2011 PositiveNegative

16. Data gathered: 1.03-31.10.2011 PositiveNegative Sentiment analysis in Social Media (blogs)

17. Negative content Data gathered: 1.03-31.10.2011

18. Research Results This study confirms that content from Social Media is valuable source of information reflecting political preferences of internet users, which finds its expression in voting for candidates from certain political party during the election. Thanks to that analysis there was possible to predict names of political parties which dominated the parliament after the election and predicted that a new party would get to the parliament for the first time. Results show that the research hypothesis was generally proven. This research confirmed a method supporting the diagnosis of the condition and dynamics of changes of parties. Therefore it can be used to influence democratic processes with the use of Social Media.

19. Next Steps Next step are: Importance of content generated by Social Media users leads its way to even deeper analysis an research including advancement in sentiment analysis Deeper social network analysis (who are the users, how they behave online, what are factors that influence them the most) Extention of the method for different fields (stock exchange, complex social processes)

20. Thank you Danke Schön Paweł Kuczma, Włodzimierz Gogołek p.kuczma@id.uw.edu.pl